Monte Carlo Simulation of Diverging Collimator Geometries for Ring SPECT/MR
Bibliographic record
Abstract
The potential of a diverging fan beam collimator for use in a multimodal SPECT-MR system has been investigated. Collimation was designed for use with a stationary ring of gamma camera modules each comprised of a 32 × 32 pixel CZT detector. The collimators provide a desired field of view (FOV) of 25.0 cm at the center of the bore. Eleven collimator designs were compared, yielding between 13 to 23 modules per ring. Each design was evaluated using reconstructed resolution and sensitivity metrics. The designs were simulated with the Monte Carlo software, GEANT4 Application for the Tomographic Emission (GATE) and tomographic reconstruction was performed with a maximum-likelihood expectation maximization (ML-EM) algorithm in MATLAB. The results showed that a practical SPECT/MR design using 18 detectors per ring with a 3.83 cm length collimator gave equivalent tomographic resolution to that of a clinical SPECT/CT system but with 7.0 times greater detection sensitivity compared to the conventional rotating dual-head camera. Resolution across the reconstructed 25 cm x 25 cm FOV did show slight non-uniformity, with resolution improving around the periphery of the FOV as much as two-fold. A smearing artifact was seen in the corners of the FOV likely due to undersampling within those regions. A reconstructed hot-rod resolution phantom matched the previous results, giving similar resolution performance. However, the simulation also showed that the system suffers from aliasing effects when reconstructing features of 7.9 mm or less. To further investigate how the design choices affected the tomographic resolution, parameters for collimator hole size, detector pixel size, and number of projection angles were explored. Both the reduction of hole size and pixel size each allowed for improved resolvability down to 7.9 mm and 6.4 mm respectively. Increasing the number of projection angles was found to remove smearing artifacts from the image, however it did not significantly change the resolution. The resolution is therefore believed to be limited by the 2.46 mm pixel size and associated pixel matched collimator. These are promising results that show that a diverging fan beam collimator could be a viable choice for a SPECT/MR system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".